Kernel name: “NYC Taxi EDA - Update: The fast & the curious”
Link: https://www.kaggle.com/headsortails/nyc-taxi-eda-update-the-fast-the-curious
In this project we are going to explore the kernel above showing why the author choose the functions used, comparing to others alternatives and we are going to emphasize the interesting points of the kernel.
library("data.table")
library("tibble")
train <- as.tibble(fread('train.csv'))
In R, read.csv is part of the regular functions and is used for load data.frame from a csv file. But when we’re dealing with a huge data.frame this function can take a long time to run.
So in this part the author used a function called fread that performs much faster than read.csv (check the time of each function using profvis!!).
After that other function should be compared: load. This function is used to load variables that have been stored in a .RData file and runs very fast comparing with read.csv and fread.
When is a good ideia to use load? When it’s possible to use a background process to update the data.frame and save it in .RData file.
Let’s take a look at the three possibilities:
library("profvis")
library("data.table")
library("tibble")
library("readr")
profvis({
# fread
train <- fread("train.csv")
# read.csv
train_readcsv <- read.csv("train.csv")
# read_csv -> from "readr" package
train_read_csv <- read_csv("train.csv")
# fread + as.tibble
train_tibble <- as.tibble(fread("train.csv"))
# loading RData
save(train_readcsv, file = "train_data.RData")
rm(train_readcsv)
load(file = "train_data.RData")
})
All the information bellow was “greped” from https://cran.r-project.org/web/packages/tibble/vignettes/tibble.html
Tibbles
“Tibbles are a modern take on data frames. They keep the features that have stood the test of time, and drop the features that used to be convenient but are now frustrating (i.e. converting character vectors to factors).”
Major points:
A brief overview of our data can summaries the descriptive statistics values of the dataset and detect abnormal items or outliers.
For the summaries
summary(train)
## id vendor_id pickup_datetime dropoff_datetime
## Length:1458644 Min. :1.000 Length:1458644 Length:1458644
## Class :character 1st Qu.:1.000 Class :character Class :character
## Mode :character Median :2.000 Mode :character Mode :character
## Mean :1.535
## 3rd Qu.:2.000
## Max. :2.000
## passenger_count pickup_longitude pickup_latitude dropoff_longitude
## Min. :0.000 Min. :-121.93 Min. :34.36 Min. :-121.93
## 1st Qu.:1.000 1st Qu.: -73.99 1st Qu.:40.74 1st Qu.: -73.99
## Median :1.000 Median : -73.98 Median :40.75 Median : -73.98
## Mean :1.665 Mean : -73.97 Mean :40.75 Mean : -73.97
## 3rd Qu.:2.000 3rd Qu.: -73.97 3rd Qu.:40.77 3rd Qu.: -73.96
## Max. :9.000 Max. : -61.34 Max. :51.88 Max. : -61.34
## dropoff_latitude store_and_fwd_flag trip_duration
## Min. :32.18 Length:1458644 Min. : 1
## 1st Qu.:40.74 Class :character 1st Qu.: 397
## Median :40.75 Mode :character Median : 662
## Mean :40.75 Mean : 959
## 3rd Qu.:40.77 3rd Qu.: 1075
## Max. :43.92 Max. :3526282
Data overview
library("dplyr")
glimpse(train)
## Observations: 1,458,644
## Variables: 11
## $ id <chr> "id2875421", "id2377394", "id3858529", "id3...
## $ vendor_id <int> 2, 1, 2, 2, 2, 2, 1, 2, 1, 2, 2, 2, 2, 2, 2...
## $ pickup_datetime <chr> "2016-03-14 17:24:55", "2016-06-12 00:43:35...
## $ dropoff_datetime <chr> "2016-03-14 17:32:30", "2016-06-12 00:54:38...
## $ passenger_count <int> 1, 1, 1, 1, 1, 6, 4, 1, 1, 1, 1, 4, 2, 1, 1...
## $ pickup_longitude <dbl> -73.98215, -73.98042, -73.97903, -74.01004,...
## $ pickup_latitude <dbl> 40.76794, 40.73856, 40.76394, 40.71997, 40....
## $ dropoff_longitude <dbl> -73.96463, -73.99948, -74.00533, -74.01227,...
## $ dropoff_latitude <dbl> 40.76560, 40.73115, 40.71009, 40.70672, 40....
## $ store_and_fwd_flag <chr> "N", "N", "N", "N", "N", "N", "N", "N", "N"...
## $ trip_duration <int> 455, 663, 2124, 429, 435, 443, 341, 1551, 2...
Another popular way to make a data overview is using str. It is very similar to glimpse but str shows less data.
str(train)
## Classes 'data.table' and 'data.frame': 1458644 obs. of 11 variables:
## $ id : chr "id2875421" "id2377394" "id3858529" "id3504673" ...
## $ vendor_id : int 2 1 2 2 2 2 1 2 1 2 ...
## $ pickup_datetime : chr "2016-03-14 17:24:55" "2016-06-12 00:43:35" "2016-01-19 11:35:24" "2016-04-06 19:32:31" ...
## $ dropoff_datetime : chr "2016-03-14 17:32:30" "2016-06-12 00:54:38" "2016-01-19 12:10:48" "2016-04-06 19:39:40" ...
## $ passenger_count : int 1 1 1 1 1 6 4 1 1 1 ...
## $ pickup_longitude : num -74 -74 -74 -74 -74 ...
## $ pickup_latitude : num 40.8 40.7 40.8 40.7 40.8 ...
## $ dropoff_longitude : num -74 -74 -74 -74 -74 ...
## $ dropoff_latitude : num 40.8 40.7 40.7 40.7 40.8 ...
## $ store_and_fwd_flag: chr "N" "N" "N" "N" ...
## $ trip_duration : int 455 663 2124 429 435 443 341 1551 255 1225 ...
## - attr(*, ".internal.selfref")=<externalptr>